A subpixel mapping algorithm combining pixel-level and subpixel-level spatial dependences with binary integer programming

A new subpixel mapping (SPM) algorithm combining pixel-level and subpixel-level spatial dependences is proposed in this letter. The pixel-level dependence is measured by the spatial attraction model (SAM) with either surrounding or quadrant neighbourhood, while the subpixel-level dependence is characterized by either the mean filter or the exponential weighting function. Both pixel-level and subpixel-level dependences are then fused as the weighted dependence in the constructed objective function. The branch-and-bound algorithm is employed to solve the optimization problem, and thus, obtain the optimal spatial distribution of subpixel classes. An artificial image and a set of real remote sensing images were tested for validation of the proposed method. The results demonstrated that the proposed method can achieve results with greater accuracy than two traditional SPM methods and the mixed SAM method. Meanwhile, the proposed method needs less computation time than the mixed SAM, and hence it provides a new solution to subpixel land cover mapping.